Futures-Driven Innovation: How to Create Meaningful Change
Innovation practice is very good at studying people as they are and that is exactly why it keeps producing incremental results. So we need to bring futures into the mix to create that impact that everyone is looking for.
Two pieces of received wisdom sit in every innovation department, and they contradict each other. The customer is always right; and customers do not know what they want. Both get quoted, often by the same people in the same week. They can both be true because they refer to different points in time. Ask people about their present frustrations and they are the best available authority. Ask them about a product category that does not exist yet, in a world whose conditions have changed, and they can only answer from where they are standing now.
This is the structural limit of innovation practice. Almost every method it relies on samples the present: interviews, observation, usability testing, minimum viable products, A/B tests. Each of these is a way of asking the world as it currently is what it thinks. That is what makes them reliable, and it is also why a process built entirely from them tends to produce better versions of what already exists.
Futures-driven innovation is the practice of adding the missing dimension: combining user and market understanding with a structured exploration of how the operating environment could change, so that what gets built is aimed at the conditions it will actually meet rather than the ones it was designed in.
The problem is rarely that nobody saw it coming
Kodak is usually told as a story about blindness. It is closer to the opposite, and the accurate version is more useful.
Steven Sasson built the first digital camera prototype at Kodak in 1975. The company held the patents and the engineering talent, and it went on to run substantial digital imaging operations. At its peak in the 1990s it held roughly 90 per cent of the US film market and a market capitalisation of about 28 billion dollars. It filed for Chapter 11 in 2012.
Kodak did not fail to see digital photography. It failed to act on what it saw, because acting meant cannibalising the most profitable business in its portfolio, and no part of the organisation was rewarded for doing that. Clayton Christensen's account of the innovator's dilemma describes the mechanism: optimising for present profit is a rational strategy that systematically prevents adaptation.
A smaller example points the same way. Steve Jobs did not resist the iPhone, which he drove. What he resisted was third-party native applications on it, arguing at the 2007 launch that web apps were the "sweet solution" and that developers did not need an SDK. The App Store arrived a year later and became one of Apple's largest businesses. The resistance was to a specific loss of control, not to the future in general.
The pattern in both is that resistance is rarely ignorance. It is a defence of something currently valuable.
Why the resistance is more stubborn than it looks
The popular explanation for change resistance is neurological, usually some version of the brain treating change as a threat. It is a comforting story and it does not survive contact with the research, which locates the effect in how people evaluate options rather than in a specific alarm circuit.
The better-established finding is status quo bias, documented by Samuelson and Zeckhauser in 1988: when one option is labelled as the current state, people choose it far more often than they choose the same option presented neutrally. Combined with loss aversion, where a loss is felt more heavily than an equivalent gain, this produces a consistent asymmetry. The costs of changing are specific, immediate and attributable to whoever decided. The benefits are diffuse, delayed and shared.
This matters practically, because it changes the intervention. If resistance were a threat response, the answer would be reassurance. Since it is closer to an accounting problem, the answer is to change what gets counted and who carries the risk. Making the cost of standing still visible is more effective than making change sound exciting.
Where foresight enters an innovation process
Foresight tends to arrive too late, as a report delivered to a team that has already chosen what to build. Three points in the process are where it actually changes the output.
Before the brief. Most innovation briefs contain an unstated assumption about what the operating environment will look like when the thing ships, and it is usually the current one. Writing that assumption down and testing it against two or three alternatives is the highest-leverage half day in the process.
At concept selection. This is where a portfolio gets narrowed, and the criteria are almost always present-tense: current market size, current willingness to pay, current regulation. Running the shortlist through a scenario set separates concepts that only work if the world stays as it is from those that survive more than one future.
After launch, as monitoring. The indicators that would tell you the environment is moving are cheap to specify while the futures work is fresh, and nearly impossible to reconstruct a year later.
Three tools we use
Scenario planning. Build a small set of plausible future operating environments, then run concepts through each one. The useful output is rarely a ranking. It is the discovery that a concept the team loved depends on one regulatory outcome, or that an unglamorous option performs adequately everywhere.
Futuresstorm. A structured session that works outwards from signals of change to drivers and then to barriers, so that the group generates from evidence of what is already moving rather than from what it can think of on the day.
Future user stories. The familiar user story form, relocated. Rather than describing what a current user needs, it describes a person living under the conditions of a specific scenario, which surfaces needs that no present-day interview could have produced.
What the butterfly effect actually says
Small changes compounding into large effects is a popular reading of chaos theory, and it gets used as encouragement but the original claim is less flattering and more useful.
Sensitive dependence on initial conditions means that in certain systems, differences too small to measure produce outcomes too different to predict. It is a statement about the limits of forecasting rather than a promise that small deliberate actions will produce the large results you intended.
Taken seriously, it argues for a particular way of working. Because you cannot know in advance which small move compounds, you run several rather than betting the organisation on one, you keep them cheap enough to abandon, and you monitor conditions closely enough to notice which one is catching. This is what innovation practice already does well through experimentation, and it is the part of it that foresight has the least reason to argue with.
The combination
Innovation supplies the method for acting under uncertainty: experiment, test, discard, repeat. Foresight supplies the direction and the time horizon, so the experiments point somewhere that will still exist. Neither is sufficient. Innovation without foresight optimises efficiently towards a world that is going away. Foresight without innovation produces reports.
We need to plan for our plans not going according to plan, and to avoid solving tomorrow's problems with today's solutions. Both are easier to say than to organise for, which is the actual work.
Sources
Steven Sasson's 1975 digital camera prototype, Kodak's market position and its 2012 Chapter 11 filing: standard corporate history, widely documented
Clayton M. Christensen, The Innovator's Dilemma, Harvard Business School Press (1997)
Steve Jobs on web apps as the "sweet solution", WWDC 2007, and the App Store launch in July 2008
William Samuelson and Richard Zeckhauser, "Status Quo Bias in Decision Making", Journal of Risk and Uncertainty 1(1): 7-59 (1988)
Daniel Kahneman and Amos Tversky on loss aversion, "Prospect Theory: An Analysis of Decision under Risk", Econometrica 47(2) (1979)
Originally published on Medium in 2023 and revised in September 2026.
Frequently asked questions
What is futures-driven innovation? An approach that combines user and market research with structured exploration of how the operating environment could change, so that innovation work is directed at future conditions rather than only at present ones. It joins foresight methods, such as scenario building and horizon scanning, to innovation methods such as prototyping and experimentation.
How is it different from user-centred innovation? User-centred innovation samples people as they are now, which makes it accurate about present needs and structurally biased towards incremental improvement. Futures-driven innovation keeps that research and adds an explicit view of how the context around the user could change over the life of the product or service.
Why do organisations resist change even when the evidence is clear? Rarely because they have not seen it. Status quo bias and loss aversion mean the costs of changing are specific, immediate and attributable, while the benefits are diffuse and delayed. When the change threatens a currently profitable line of business, no internal incentive rewards the person who proposes it.
Where should foresight sit in an innovation process? Three points: before the brief, to test the assumed operating environment; at concept selection, to separate concepts that need the world to stay as it is; and after launch, to define the indicators that will signal the environment moving.
What tools connect foresight and innovation? Scenario planning for testing concepts against several futures, Futuresstorm for generating from signals of change rather than from opinion, and future user stories for describing needs that arise under specific future conditions.
How do we anticipate change when people do not behave as the data says they should?
Culture often signals what is shifting before it shows up clearly in reports, forecasts, or dashboards. It shapes how change is interpreted, resisted, adapted, and made part of everyday life. That is why cultural foresight matters. It helps us look beyond surface trends and understand the values, tensions, narratives, and behaviours that make futures feel plausible, desirable, or difficult to accept.
In this practical masterclass, futurist Mathias Behn Bjørnhof (ANTICIPATE) and cultural foresight practitioner Chloé de Ruffray explore how cultural foresight can strengthen strategic foresight by bringing in the human layers too often left out. The session moves from why culture matters in foresight to how to work with it in practice, showing how lived realities, shared meanings, expectations, trust, resistance, and emerging behaviours can all become valuable signals of change.
Across the replay, you will be introduced to a set of cultural foresight tools and ways of thinking, including fringe mapping, Causal Layered Analysis, Thing from the Future, and Ethnographic Futures Research. Together, these methods help move from scanning the edges, to sensing deeper patterns, to imagining more grounded and tangible futures, and finally to translating those insights into strategic reflection and action.
This replay is for strategists, innovation teams, brand and communication professionals, researchers, and anyone curious about how to understand change in a more human, nuanced, and futures-oriented way.
Replay includes:
Recording, slides/workbook, and Q&A recording.
By purchasing this digital product, you confirm that you agree to the following statement: "I agree that delivery begins immediately upon payment, and I understand and accept that my 14-day right of withdrawal expires once delivery has begun.” (see Return Policy for more information)
